Using fluorescence images from live cells, researchers have trained an artificial neural network to recognise cells that are infected by adenoviruses or herpes viruses.
When Bumrungrad International Hospital first went live with the InterSystems TrakCare electronic medical record system, its clinical laboratory took a huge leap forward.
A PhD student at the University of Canterbury has been working on computational algorithms that can automatically read and analyse mammogram X-rays.
Researchers have developed a technique that uses machine learning to determine whether a single cell is healthy or cancerous by analysing its pH.
The crappifier takes high-quality images and computationally degrades them, so that they look something like the lowest low-resolution images available.
Diffusion weighted imaging and machine learning can successfully classify the diagnosis and characteristics of common types of paediatric brain tumours.
Launceston General Hospital has adopted ISBT 128 with a digital interface to the BloodNet online ordering and inventory management system.
The ViTAM-116 is a fully sealed, 15.6″, IP66/IP69K, stainless steel, Full HD industrial monitor. To comply with IP66/IP69K standards the device uses waterproof sealed connectors for all I/O connections.
Researchers have successfully used machine learning to complete cumbersome materials science calculations more than 40,000 times faster than normal.
Researchers have found two new mammogram-based measures for breast cancer risk, which could improve the screening process and make it less stressful for women.
Using machine learning, researchers have identified distinct patterns of coordinated activity between different parts of the brain in people with major depressive disorder.
Researchers used field data to 'train' a machine-learning model to detect gamba grass from high-resolution, multispectral satellite imagery.
Image recognition AI has the potential to revolutionise medical diagnostics, yet its current value proposition remains below the expectations of most radiologists.